Forecasting the effect of traffic control strategies in railway systems: A hybrid machine learning method

Estimating the impacts of traffic control strategies (TCSs) can provide feedback in traffic control and help to identify the effective ones among massive strategies, thus boosting intelligent railway systems. This paper aims to assess the impacts of TCSs in railway systems from two perspectives: given a TCS on the target trains, for the subsequent trains, (1) how many control actions are needed in the future? (2) how many changes are there in terms of the average train delay? To this end, a hybrid learning model combining a convolutional neural network (CNN) and the random forest (RF), named CNN-RF, was innovatively proposed. The CNN component extracts features from the data, while the RF component predicts the targets (i.e., the effect measurements), to ensure the model’s performance. The proposed model was evaluated based on the real-world train operation data from Chinese high-speed railways, and the average values of metrics f1-score, g-means, MAE (mean absolute error), RMSE (root mean square error), and R2 (goodness of fit) reach 0.756, 0.762, 0.488 min, 1.035 min, and 0.795, respectively. Compared with the benchmarks, the proposed model improves the above metrics by 10.52% on average. These results demonstrate that the proposed model effectively predicts the impacts of TCSs in the near future, facilitating rail traffic control and potentially improving the quality of transportation services.